• Login
    View Item 
    •   Home
    • The Christie Research Publications Repository
    • All Christie Publications
    • View Item
    •   Home
    • The Christie Research Publications Repository
    • All Christie Publications
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of ChristieCommunitiesTitleAuthorsIssue DateSubmit DateSubjectsThis CollectionTitleAuthorsIssue DateSubmit DateSubjectsProfilesView

    My Account

    LoginRegister

    Local Links

    The Christie WebsiteChristie Library and Knowledge Service

    Statistics

    Display statistics

    Clinical evaluation of deep learning autocontouring in prostate and head and neck cancer

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Authors
    Hague, Christina
    Beasley, William J
    McPartlin, Andrew J
    Owens, Susan E
    Price, Gareth J
    Saud, H.
    Slevin, Nicholas J
    Van Herk, Marcel
    Whitehurst, Philip
    Chuter, Robert
    Affiliation
    The Christie NHS Foundation Trust, Clinical Oncology, Manchester,
    Issue Date
    2020
    
    Metadata
    Show full item record
    Abstract
    Purpose or Objective Manually contouring organs at risk (OARs) is time consuming and affected by inter-observer variability. As the complexity and number of OARs increases the role of auto-contouring to standardise delineation and reduce clinician workload becomes increasingly important. The aim of this study was to evaluate the ability of deeplearning based auto-contouring to produce clinically acceptable OAR contours. Material and Methods Two Head and Neck (H&N) models, A and B trained on local data and two “generic” models trained on data from other centres (one H&N and one prostate model) were evaluated. OAR contours from ten randomly selected H&N patients and nine prostate patients were reviewed. Autocontours (DLCExpert™, Mirada Medical) were reviewed by two independent observers and scored from 1-7 according to a ‘goodness of fit’ descriptive category. Scores of 5 (“requiring 20-50% manual edits to meet clinical standards”) or less were defined as acceptable. To compare contours generated by the four models with manual contours distances to agreement (DTA) were calculated. For the prostate model, median, minimum and maximum time required for manual contouring was recorded and compared with the time required to edit to DLC-expert generated contours. Results Manual editing of contours generated by the DLC-expert model saved time compared with full manual contouring for all prostate OARs, and in particular the bladder (Table 1). Average goodness-of-fit scores were similar between the two independent observers as shown in Table 2. The generic model met clinical standards for the mandible, oral cavity, brainstem and left submandibular gland and outperformed models A and B, in particular for left and right submandibular glands (3.9 vs 12.1 mm and 3.1 vs 3.8 mm DTA). However for brainstem, spinal cord, larynx, bilateral parotid glands and eyes, local models A and B performed better (e.g. 2.8 vs 4.2 mm for brainstem and 6.6 vs 11.5 mm for spinal cord). Irrespective of the model, contours generated were not clinically acceptable for the optic chiasm, optic nerves and pharyngeal constrictor muscles requiring >50% manual edits. Conclusion A “generic” deep learning model has been shown to aid the clinical workflow by reducing the time taken to delineate OARs for prostate patients. Auto-contouring for small, poorly visualised structures on CT such as the optic apparatus, however, has poor performance. The integration of MR in the contouring of such structures may be a solution but this remains to be validated. For H&N the DTA and clinical acceptability showed that contours from a mixture of local and generic models would potentially give clinically acceptable contours. Standard models can be very useful if they match internal contouring guidelines. Clinical evaluation of these and other models is ongoing within the centre.
    Citation
    Hague C, Beasley W, McPartlin A, Owens S, Price G, Saud H, et al. PO-1719: Clinical evaluation of deep learning autocontouring in prostate and head and neck cancer. Radiotherapy and Oncology . 2020 Nov;152:S950. 
    Journal
    Radiotherapy and Oncology
    URI
    http://hdl.handle.net/10541/624169
    Type
    Meetings and Proceedings
    Language
    en
    Collections
    All Christie Publications

    entitlement

     
    DSpace software (copyright © 2002 - 2025)  DuraSpace
    Quick Guide | Contact Us
    Open Repository is a service operated by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.